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Dr Haris Pervaiz

Research Fellow
PhD in Communication Systems

Academic and research departments

Faculty of Engineering and Physical Sciences.


My qualifications

PhD in Communication Systems
Lancaster University, UK
MPhil in Electrical & Electronic Engineering
Queen Mary University of London, UK
MSC in Information Security
Royal Holloway University of London, UK
Bachelor in Computer Software Engineering
NUST, Pakistan


Research interests

Research projects

Research collaborations

My publications


Onireti Oluwakayode, Mohamed Abdelrahim, Pervaiz Haris bin, Imran Muhammad (2017) Analytical Approach to Base Station Sleep Mode Power Consumption and Sleep Depth,Proceedings of 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) IEEE
In this paper, we present an analytical framework
to model the sleep mode power consumption of a base
station (BS) as a function of its sleep depth. The sleep depth
is made up of the BS deactivation latency, actual sleep period
and activation latency. Numerical results demonstrate
a close match between our proposed approach and the
actual sleep mode power consumption for selected BS types.
As an application of our proposed approach, we analyze the
optimal sleep depth of a BS, taking into consideration the
increased power consumption during BS activation, which
exceeds its no-load power consumption. We also consider
the power consumed during BS deactivation, which also
exceeds the power consumed when the actual sleep level is
attained. From the results, we can observe that the average
total power consumption of a BS monotonically decreases
with the sleep depth as long as the ratio between the actual
sleep period and the transition latency (deactivation plus
reactivation latency) exceeds a certain threshold.
Mohamed A, Onireti O, Imran M, Pervaiz H, Xiao P, Tafazolli R (2017) Predictive Base Station Activation in Futuristic Energy-Efficient Control/Data Separated RAN,IEEE Globecom 2017 Proceedings IEEE
Nowadays, system architecture of the fifth generation
(5G) cellular system is becoming of increasing interest.
To reach the ambitious 5G targets, a dense base station (BS)
deployment paradigm is being considered. In this case, the
conventional always-on service approach may not be suitable due
to the linear energy/density relationship when the BSs are always
kept on. This suggests a dynamic on/off BS operation to reduce
the energy consumption. However, this approach may create
coverage holes and the BS activation delay in terms of hardware
transition latency and software reloading could result in service
disruption. To tackle these issues, we propose a predictive BS
activation scheme under the control/data separation architecture
(CDSA). The proposed scheme exploits user context information,
network parameters, BS sleep depth and measurement databases
to send timely predictive activation requests in advance before
the connection is switched to the sleeping BS. An analytical model
is developed and closed-form expressions are provided for the
predictive activation criteria. Analytical and simulation results
show that the proposed scheme achieves a high BS activation
accuracy with low errors w.r.t. the optimum activation time.
Akbar A, Kousiouris G, Pervaiz H, Sancho J, Ta-Shma P, Carrez F, Moessner K (2018) Real-time Probabilistic Data Fusion for Large-scale IoT Applications,IEEE Access 6 pp. 10015-10027 Institute of Electrical and Electronics Engineers (IEEE)
IoT data analytics is underpinning numerous applications,
however the task is still challenging predominantly due
to heterogeneous IoT data streams, unreliable networks and ever
increasing size of the data. In this context, we propose a two layer
architecture for analyzing IoT data. The first layer provides a
generic interface using a service oriented gateway to ingest data
from multiple interfaces and IoT systems, store it in a scalable
manner and analyze it in real-time to extract high-level events
whereas second layer is responsible for probabilistic fusion of
these high-level events. In the second layer, we extend state-ofthe-
art event processing using Bayesian networks (BNs) in order
to take uncertainty into account while detecting complex events.
We implement our proposed solution using open source components
optimized for large-scale applications. We demonstrate
our solution on real-world use-case in the domain of intelligent
transportation system (ITS) where we analysed traffic, weather
and social media data streams from Madrid city in order to
predict probability of congestion in real-time. The performance
of the system is evaluated qualitatively using a web-interface
where traffic administrators can provide the feedback about the
quality of predictions and quantitatively using F-measure with
an accuracy of over 80%.
Raza Naqvi Syed Ahsan, Pervaiz Haris, Ali Hassan Syed, Musavian Leila, Ni Qiang, Imran Muhammad Ali, Ge Xiaohu, Tafazolli Rahim (2018) Energy-Aware Radio Resource Management in D2D-Enabled Multi-Tier HetNets,IEEE Access 6 pp. 16610-16622 Institute of Electrical and Electronics Engineers (IEEE)
Hybrid networks consisting of both millimeter
wave (mmWave) and microwave (¼W) capabilities are
strongly contested for next generation cellular communications.
A similar avenue of current research is deviceto-
device (D2D) communications, where users establish
direct links with each other rather than using central base
stations (BSs). However, a hybrid network, where D2D
transmissions coexist, requires special attention in terms
of efficient resource allocation. This paper investigates
dynamic resource sharing between network entities in a
downlink (DL) transmission scheme to maximize energy
efficiency (EE) of the cellular users (CUs) served by either
(¼W) macrocells or mmWave small cells, while maintaining
a minimum quality-of-service (QoS) for the D2D
users. To address this problem, firstly a self-adaptive power
control mechanism for the D2D pairs is formulated, subject
to an interference threshold for the CUs while satisfying
their minimum QoS level. Subsequently, a EE optimization
problem, which is aimed at maximizing the EE for both
CUs and D2D pairs, has been solved. Simulation results
demonstrate the effectiveness of our proposed algorithm,
which studies the inherent tradeoffs between system EE,
system sum rate and outage probability for various QoS
levels and varying density of D2D pairs and CUs.
Munir Hamnah, Pervaiz Haris bin, Hassan Syed Ali, Musavian Leila, Ni Qiang, Imran Muhammad Ali, Tafazolli Rahim (2018) Computationally Intelligent Techniques for Resource Management in mmWave Small Cell Networks,IEEE Wireless Communications 25 (4) pp. 32-39 Institute of Electrical and Electronics Engineers (IEEE)
Ultra densification in heterogeneous networks
(HetNets) and the advent of millimeter wave (mmWave) technology
for fifth generation (5G) networks have led the researchers
to redesign the existing resource management techniques. A
salient feature of this activity is to accentuate the importance
of computationally intelligent (CI) resource allocation schemes
offering less complexity and overhead. This paper overviews the
existing literature on resource management in mmWave-based
HetNets with a special emphasis on CI techniques and further
proposes frameworks that ensure quality-of-service requirements
for all network entities. More specifically, HetNets with mmWavebased
small cells pose different challenges as compared to an allmicrowave-
based system. Similarly, various modes of small cell
access policies and operations of base stations in dual mode, i.e.,
operating both mmWave and microwave links simultaneously,
offer unique challenges to resource allocation. Furthermore, the
use of multi-slope path loss models becomes inevitable for analysis
owing to irregular cell patterns and blocking characteristics of
mmWave communications. This paper amalgamates the unique
challenges posed because of the aforementioned recent developments
and proposes various CI-based techniques including game
theory and optimization routines to perform efficient resource
Onireti Oluwakayode, Mohamed Abdelrahim, Pervaiz Haris, Imran Muhammad (2018) A Tractable Approach to Base Station Sleep Mode Power Consumption and Deactivation Latency,Proceedings of IEEE 29th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) Institute of Electrical and Electronics Engineers (IEEE)
We consider an idealistic scenario where the
vacation (no-load) period of a typical base station (BS)
is known in advance such that its vacation time can be
matched with a sleep depth. The latter is the sum of
the deactivation latency, actual sleep period and reactivation
latency. Noting that the power consumed during
the actual sleep period is a function of the deactivation
latency, we derive an accurate closed-form expression
for the optimal deactivation latency for deterministic BS
vacation time. Further, using this expression, we derive the
optimal average power consumption for the case where
the vacation time follows a known distribution. Numerical
results show that significant power consumption savings
can be achieved in the sleep mode by selecting the
optimal deactivation latency for each vacation period.
Furthermore, our results also show that deactivating the
BS hardware is sub-optimal for BS vacation less than a
particular threshold value.
Pervaiz Haris, Onierti Oluwakayode, Mohamed Abdelrahim, Imran Muhammad, Qiang Ni, Tafazolli Rahim (2018) Energy-Efficient and Load-Proportional eNodeB for 5G User-Centric Networks,IEEE Vehicular Technology Magazine 13 (4) pp. pp 51-59 Institute of Electrical and Electronics Engineers (IEEE)
Nowadays, dense network deployment is being
considered as one of the effective strategies to meet capacity
and connectivity demands of the fifth generation (5G) cellular
system. Among several challenges, energy consumption will be a
critical consideration in the 5G era. In this direction, base station
on/off operation, i.e., sleep mode, is an effective technique to
mitigate the excessive energy consumption in ultra-dense cellular
networks. However, current implementation of this technique is
unsuitable for dynamic networks with fluctuating traffic profiles
due to coverage constraints, quality-of-service requirements and
hardware switching latency. In this direction, we propose an
energy/load proportional approach for 5G base stations with
control/data plane separation. The proposed approach depends on
a multi-step sleep mode profiling, and predicts the base station
vacation time in advance. Such a prediction enables selecting
the best sleep mode strategy whilst minimizing the effect of
base station activation/reactivation latency, resulting in significant
energy saving gains.